| abstract |
The present invention propose towards higher-dimension and unbalanced data classify it is integrated, it is characterised in that using dimensionality reduction and the sequencing of sampling, pretreatment strategy is reduced to two classes；Reproducibility principle based on experiment conclusion, some standard data sets for choosing data mining and machine learning are used as experimental data；In the selection of preprocess method, packaged type (Wrapper) feature selection approach and over sampling method are added；The influence of dependence number and the uneven aspect of degree two research preprocess method to higher-dimension unbalanced data classification performance, using more complete Pretreatment Test strategy, obtains different conclusions：Before classifying to higher-dimension unbalanced data, the average AUC performances for first reducing the generation of feature releveling data are more excellent, and automaticity is strong, and higher-dimension and the uneven influence to classification are relaxed using different pretreatment combination size strategies. |